In general, a video image includes massive amounts of data. It is infeasible to communicate and to store a video image directly. Examples of communicating video images are videoconferences, video telephones and remote teaching, etc. Examples of storing video images are multimedia databases, VCD and DVD, etc. In a video service system, except video data, there are also audio data, T.120 data and control information, etc. At the same time, it is highly required that a video service system is real time, interactive and having good image quality. Therefore, the video image data should be compressed with a high compression rate. At present, there are international standards for video image compression. H.261 and H.263 are standardized by ITU-T, and MPEG1, MPEG2 and MPEG4 are standardized by ISO. In these standards, some present important compression techniques are involved, and they have many common issues, such as they all use Common Intermediate Format (CIF), they all use hybrid mode of Motion-Compensated Prediction and Discrete Cosine Transform (DCT), etc.
In a video system, implemented with the above standards, it is unavoidable that an acquired video image involves some noise. For example, there are high-frequency impulse noise, caused by the large changes in luminance components and chrominance components at a small region of an image, and random noise, generated by A/ID conversion and quantization during signal sampled. Without suppressing the noise before compression, the compression efficiency will be greatly decreased. Therefore, in order to have a better compression efficiency and a good image quality, it is necessary to have a pre- and post-processing to reduce or eliminate the noise in an image.
There are many existing methods for suppressing noise. In general, each one of them uses an adequate filtering method in the spatial domain or frequency domain. The filtering methods can be divided into: linear filtering, such as one-dimension finite impulse response (1-D FIR) filtering, two-dimensions finite impulse response (2-D FIR) filtering, etc., and non-linear filtering, such as median filtering, threshold filtering, etc.
Reference to the U.S. Pat. No. 5,787,203 patent, titled “Method and system for filtering compressed video images”, which discloses a method for filtering in the spatial domain. A nonlinear filtering method is used for the differences of images after motion-compensated prediction and before DCT. There are two times of filtering: first a threshold filter that reduces or eliminates random noise, then a cross-shaped median filter that reduces or eliminates high frequency impulse noise. They reduce the overall compression bitrate by 10% to 20%. Reference to the U.S. Pat. No. 5,325,125 patent, titled “Intra-frame filter for video compression systems”, which discloses a method with linear filtering directly before compression. The method applies a two dimensional (2-D) filter to filter out high frequency components and high frequency impulse noise in the image diagonal direction.
In order to reduce or eliminate noise effectively, in the present suppressing noise methods, a one-dimension or two-dimension filtering process is added in the original processing procedure. Nevertheless, in general, a filtering calculation takes time and is a heavy load for a system. So, for the video conference system, which highly requires real time and interactive, it is necessary to have an suppressing noise method which does not take time and keeps the original image as much as possible. In the present filtering technology, the threshold filtering is a method with less amount of calculation. The basic principle of this method is: at a transmitting end, quantizing DCT coefficients of a CIF image blocks is in preset sequence; when a DCT coefficient is equal or less than a predetermined threshold, the DCT coefficient is set to zero; and when a DCT coefficient is greater than the predetermined threshold, the DCT coefficient is unchanged. In essence, the method of pre-suppressing noise is a constant threshold filtering method. The disadvantage of these pre-suppressing noise methods is that selection of a threshold is conflict. If a smaller threshold is selected, a higher image quality can be obtained; but with too small number of DCT coefficient zero value, the compression efficiency is not satisfied. If a larger threshold is selected, the compression efficiency is satisfied; but with too many number of DCT coefficient zero value, the image quality is not satisfied.